Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package which includes all methods discussed in this paper.

翻译：可视化是传播任何旨在估计因果关系的研究结果的一个关键方面。在有时间到活动结果的研究中,最受欢迎的可视化方法是描绘由利益变数组成的生存曲线。当利息变量持续时,无法使用这种方法。简单的变通办法,如对连续的共变和图绘制每个类别的生存曲线进行分类,可能导致对主要效应的误导性描述。相反,我们提议一个新的图形,即生存区图,直接描绘时间和连续共变的功能。这个图案利用基于适当时间到活动模型的计算方法获取相关估计数。通过使用 g-comput,这些估计数可以调整为不作额外努力的混结,允许根据标准的因果关系识别假设进行因果关系解释。如果这些假设得不到实现,则拟议的图案仍可用于描述非因果关系。我们用大型德国观测研究数据来说明和比较拟议的图形,以更简单的替代方法获取相关估计数。通过使用g-时间到的时间到的模型模型,可以调整这些估计数,而无需额外努力,允许根据标准的因果关系可识别性假设进行因果关系解释。如果这些假设没有达到,则拟议的图案可能被用于描述非因果关系的关联性关联性关联。我们讨论过的图案的图案所使用的图案包括了“Brachivodal ”中我们所研读所有的图案的图案的图案的图本的图的图本的图案。</s>